Embeddings and Tokenization for AI Product Managers: A Practical Guide

If you are becoming an AI Product Manager, you do not need to memorize every machine learning formula. But you should understand what happens between a user’s prompt and an AI model’s response.

One of the most important concepts to understand is embedding.

When a user enters text into an AI application, the system cannot simply treat that text like a human does. The text is processed into tokens, and those tokens are represented numerically as vectors. These vector representations, commonly called embeddings, help AI systems work with meaning, similarity, search, recommendations, and other language-related tasks.

This guide explains tokenization and embeddings in practical product terms.

What Are Embeddings in AI?

An embedding is a numerical representation of information that captures relationships and semantic similarity.

A simple way to think about it is:

Text → Tokens → Vectors/Embeddings → AI processing → Output

Computers ultimately work with numbers. Traditional software can represent text as characters and binary data, but AI systems need richer numerical representations to capture relationships between pieces of information.

For example, consider these terms:

• Cat
• Dog
• Laptop
• Computer

If their vector representations place cat and dog relatively close together, while laptop and computer are also close together, the model can represent useful relationships between concepts.

The actual embedding values are much more complex than a single number. They typically contain many dimensions.

Why Embeddings Matter for AI Products

Embeddings are especially important when you build AI features that need to understand meaning rather than exact words.

Common applications include:

1. Semantic Search

Suppose a document contains the word “kitty”, but a user searches for “cat.”

A traditional exact-text search may fail to find the relevant passage because the exact word cat is absent.

A semantic search system using embeddings can identify that cat and kitty are related concepts and retrieve the relevant content.

For an AI Product Manager, this distinction is critical when defining search requirements:

Keyword search asks: “Does this exact term exist?”

Semantic search asks: “Does this content mean something similar to what the user is asking?“

2. Recommendations

Embeddings can represent products, articles, queries, users, or other content in a numerical space.

Items with similar representations can then be used to support recommendation experiences.

For example, an AI-powered learning platform could represent course descriptions as embeddings and recommend courses that are semantically related to what a learner is exploring.

3. Grouping and Clustering

Embeddings can also help identify similar pieces of content.

A product team could use embeddings to group:

• Customer feedback
• Support tickets
• Product reviews
• Search queries
• Knowledge-base articles

Instead of manually defining every possible category, similar content can be grouped based on its numerical representation.

What Is Tokenization?

Before text becomes an embedding, it first goes through tokenization.

Tokenization is the process of breaking text into smaller units called tokens.

Importantly, tokens are not necessarily the same as words.

For example, a word that is common in a model’s vocabulary may be represented as one token, while an unfamiliar or less common word may be split into multiple tokens.

The exact tokenization depends on the model and its tokenizer.

So the simplified pipeline is:

User input
    ↓
Tokenization
    ↓
Tokens
    ↓
Embeddings / numerical representations
    ↓
Model processing
    ↓
Response

Different models can tokenize the same text differently. This matters because tokenization affects both model behavior and, for usage-priced APIs, the amount of text processed and therefore potentially the cost.

Tokens Are Not the Same as Words

A common misconception is:

100 words = 100 tokens

That is not necessarily true.

A useful rule of thumb from the reference material is that one token is approximately three-quarters of a word on average, but this is not exact and varies by model, language, and text.

A piece of text can contain more tokens than words when it includes:

• Uncommon words
• Subwords
• Punctuation
• Symbols
• Emojis
• Other text patterns

For AI Product Managers, this is useful when estimating context size, latency, and API costs.

From Embeddings to Vectors

An embedding can be represented as a vector—a list of numerical values.

Imagine a simplified two-dimensional space:

          ↑
          |       Dog
          |      •
          |    • Cat
          |
          |                  • Laptop
          |                    • Computer
          +------------------------------→

This is only a conceptual illustration.

Real embedding models generally operate in spaces with hundreds or thousands of dimensions.

The important product concept is not the visualization itself. It is that similar concepts can occupy nearby regions of the embedding space.

What Does “Dimension” Mean?

A dimension is essentially one numerical component of an embedding vector.

For example:

[0.12, -0.43, 0.87]

is a three-dimensional vector.

Real-world embedding vectors can be much larger.

The reference discusses OpenAI’s text-embedding-3-small and text-embedding-3-large, with embedding sizes described as 1,536 and 3,072 dimensions, respectively. The exact configuration available can depend on the model and API.

More dimensions do not automatically mean a better product.

An AI Product Manager should ask:

• Do we actually need the additional representation capacity?
• What accuracy improvement does it provide?
• What does it cost?
• Does it affect storage requirements?
• What are the latency implications?
• Is the improvement meaningful for the user experience?

This is a classic product trade-off, not simply a technical specification.

Embeddings Capture Context

Embeddings are useful because they can represent relationships between concepts.

Consider the word “Python.”

It can refer to a programming language or a snake.

The surrounding context helps determine which meaning is relevant.

For example:

“My favorite programming language is Python.”

has a very different meaning from:

“The python is one of the largest snakes.”

For AI products, this demonstrates why context matters when designing search, retrieval, classification, and recommendation experiences.

A Simple Example of Vector Relationships

A famous conceptual example in embeddings is:

King - Man + Woman ≈ Queen

This should not be interpreted as a literal universal formula. Rather, it illustrates how relationships between concepts can emerge in a learned vector space.

The broader takeaway for an AI Product Manager is:

Embeddings are not simply IDs assigned to words. They are numerical representations designed to capture useful relationships in the data.

How an AI Product Manager Should Think About Embeddings

You probably do not need to implement an embedding model from scratch.

Instead, focus on understanding when embeddings create product value.

Product Question 1: Do We Need Semantic Understanding?

If users search with natural language and exact keyword matching produces poor results, embeddings may be relevant.

Product Question 2: What Is the Retrieval Unit?

Decide whether you are embedding:

• Individual documents
• Paragraphs
• Product descriptions
• Support tickets
• Course content
• FAQs
• Customer feedback

The choice affects retrieval quality.

Product Question 3: What Is the Accuracy Requirement?

A larger embedding model may provide benefits, but the team should validate those benefits against real user queries rather than assuming that “larger” automatically means “better.”

Product Question 4: What Is the Cost?

Embedding thousands or millions of records creates storage and processing costs. Re-embedding content when models change can also create operational work.

Product Question 5: How Will Success Be Measured?

For an AI search feature, useful metrics could include:

• Search success rate
• Retrieval relevance
• Click-through rate
• Zero-result rate
• Answer accuracy
• Task completion rate
• Latency
• Cost per query

The embedding model is only one component of the overall product.

Embeddings vs. Tokenization: What’s the Difference?

Concept	What it does	Why an AI PM should care
Tokenization	Breaks text into tokens	Affects context, processing, and potentially cost
Token	A unit produced by a tokenizer	Determines how text is represented to the model
Embedding	Represents information numerically	Enables semantic similarity and retrieval
Vector	Ordered numerical representation	Provides the structure used for comparison
Dimension	Number of values in a vector	Creates trade-offs involving representation, storage, and performance

Practical AI Product Manager Example

Imagine you are building an AI knowledge assistant for a company.

The company has 20,000 internal documents.

A user asks:

“What is our parental leave policy?”

A basic keyword system may look for the exact phrase “parental leave.”

An embedding-based retrieval system can represent the user’s question and document passages as vectors and identify semantically related content—even when the document uses somewhat different wording.

A simplified architecture could look like:

Company Documents
       ↓
   Chunk Content
       ↓
 Create Embeddings
       ↓
 Vector Storage
       ↓
User Question
       ↓
Create Query Embedding
       ↓
Similarity Search
       ↓
Relevant Content
       ↓
LLM
       ↓
Answer

This is one of the foundations behind many Retrieval-Augmented Generation (RAG) systems.

What AI Product Managers Should Learn

You do not need to become a machine learning researcher to work effectively on AI products.

You should, however, be comfortable discussing:

1. Tokenization — how text is broken into tokens.
2. Embeddings — how information is represented numerically.
3. Vector similarity — how related content can be identified.
4. Dimensions — how embedding size affects system design.
5. Semantic search — how meaning-based retrieval differs from keyword search.
6. Cost and latency — how technical choices affect product economics.
7. Evaluation — how to determine whether the AI feature actually works for users.

Final Takeaway

Embeddings are one of the foundational concepts behind modern AI applications.

The key mental model is simple:

Text → Tokens → Numerical representations → Semantic relationships → AI application

For an AI Product Manager, the goal is not to memorize every mathematical detail behind embeddings. The goal is to understand how embeddings enable product capabilities such as semantic search, recommendations, clustering, and retrieval—and how model choice affects accuracy, cost, latency, and user experience.

Once you understand embeddings, many AI product architectures become much easier to reason about.

Frequently Asked Questions

What are embeddings in AI?

Embeddings are numerical vector representations of information that allow AI systems to work with relationships and semantic similarity between pieces of content.

What is tokenization in AI?

Tokenization is the process of breaking text into smaller units called tokens before the text is processed by an AI model.

Are tokens the same as words?

No. A token may represent a whole word, part of a word, punctuation, or another text unit. Tokenization varies between models.

Why are embeddings important for AI Product Managers?

Embeddings power or support important AI product capabilities such as semantic search, recommendations, clustering, and retrieval. Understanding them helps PMs make better decisions about quality, cost, latency, and architecture.

Does a higher embedding dimension mean better results?

Not automatically. A larger vector can provide a richer representation, but the right choice depends on the application’s accuracy requirements, cost, storage, and performance constraints.

What is the difference between keyword search and semantic search?

Keyword search primarily looks for matching terms. Semantic search uses representations such as embeddings to identify content that is conceptually or meaningfully related to the query.